Crime and Education in a Model of Information Transmission

Abstract

We model the decisions of young individuals to stay in school or drop out and engage in criminal activities. We build on the literature on human capital and crime engagement and use the framework of Banerjee (1993) that assumes that the information needed to engage in crime arrives in the form of a rumour and that individuals update their beliefs about the profitability of crime relative to education. These assumptions allow us to study the effect of social interactions on crime. In our model, we investigate informational spillovers from the actions of talented students to less talented students. We show that policies that decrease the cost of education for talented students may increase the vulnerability of less talented students to crime. The effect is exacerbated when students do not fully understand the underlying learning dynamics.

The Long-Run Impacts of Financial Aid: Evidence from California’s Cal Grant

“…There is significant evidence that state aid programs, whether through merit-based, need-based, or hybrid programs, can increase college attendance rates and completion rates, though results vary by state (Castleman and Long 2016;Cornwell, Mustard, and Sridhar 2006;Dynarski 2000Dynarski , 2004Dynarski , 2008Kane 2003;Scott-Clayton 2011;Singell and Stone 2002;and Van Der Klaauw 2002). 5 Merit aid may also increase human capital accumulation if it produces additional effort or alters students' use of time by, for example, reducing the hours needed to work (DesJardins et al 2010).…”

Partners in Crime

“…1 Peer effects in criminal activity have been found within neighborhoods, schools, and juvenile corrections facilities (Ludwig, Duncan, and Hirshfield 2001;Kling, Ludwig, and Katz 2005;Ludwig and Kling 2007;Bayer, Hjalmarsson, and Pozen 2009;Patacchini and Zenou 2009;Deming 2011;Billings, Deming, and Rockoff 2014). 2 The available evidence suggests that concentrating disadvantaged youth together in the same environment leads to more total crime (Jacobson 2004;Cook and Ludwig 2005;Carrell and Hoekstra 2010;Deming 2011;Imberman, Kugler, and Sacerdote 2012;Billings, Deming, and Rockoff 2014;Damm and Dustmann 2014).…”

Leaders in juvenile crime

“…For each individual, we then use the friendship links to calculate the distance to a leader. 18 A given individual may not even know (or ever meet) the leader, who can be someone in a different grade/class or of a different race/sex. In our sample, friendship networks are dense: roughly 40% of the students are directly or indirectly connected to the leaders through a friendship chain (e.g., friends of friends of friends).…”

“…Our purpose is to single out the most notable criminals, and the fact that there may be more than one leader in a network makes the definition of the "distance to the leader" more meaningful.17 In Section 6.1 below, we provide a robustness check using a different definition of a criminal leader. We use a more general measurement by considering the top 10 percentile of individual crime index distribution in each school 18. While we consider all networks (including those having more than one individual with an extreme level of crime), each student is uniquely matched to one leader, the one who is his or her closest (in terms of geodesic distance) leader in the network.…”